Informatics
References 1 Paul, SM et al (2010). How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 9, 203-214. 2 Petrillo, AL. Lean thinking for drug discovery – better productivity for pharma, Drug Discovery World, Spring 2007, 9-14. 3 Houston, JG et al. Technologies for Improving Lead Optimisation. American Drug Discovery,1 (3), Oct/Nov 2006, page 6-15. 4 Hammond, C and O’Donnell, CJ (2008). Lean six sigma – its application to drug discovery. Drug Disc. World Spring 11-18. 5 Andersson, S et al (2009). Making medicinal chemistry more effective – application of lean sigma to improve processes speed and quality. Drug
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These layers of complexity can be exacerbated when experiments are being conducted by multi- ple external organisations such as CROs. All of these issues can be overcome with good opera- tional definitions, but it is helpful to consider and control these sources of variation upfront before implementing visualisations to avoid confusion and misinterpretation later. The example funnels (Figure 2) are representations of attrition and average cycle times (we have used mean times, but note that medians are often chosen to reduce the effect of outliers). The need to average the cycle times for simplicity of visualisation comes at the cost of loss of important insight on variation. From a process perspective, it is usually more desir- able to have predictable cycle times with low varia- tion, rather than an apparently short average time with high underlying variation, since reliable times enable forward planning. In our experience, at the outset of this kind of process work, cycle times are long and variation is high, and so after the first cycle of intervention, it is often common and sufficient to measure reduction in cycle time as the primary goal. However, as an improvement system evolves and matures, reduction in variation around the average becomes more prominent. Consequently, one of the possible future developments of the funnel represen- tations could be representation of the underlying variation (see next section).
Perhaps the most important issue arising from open and transparent visualisations of processes is the human aspect. Organisational management cultures tend to emphasise performance manage-
ment, and these principles are exerted at the indi- vidual level, with employees having salaries and/or bonuses awarded on the grounds of individual performance, albeit sometimes within a team con- text. Understandably, this predominant culture can give rise to friction due to an individual’s sen- sitivities about individually attributable work- flows being made openly available for scrutiny. Furthermore, department leaders can feel exposed if it appears that ‘their’ department has longer cycle times than others. It is unfortunate that these sensitivities arise, since the principles which underpin the desire to make the process perform- ance visible and transparent have, at their heart, the idea that it is the system and process which is being examined and improved, not the individual or team18,19. Therefore substantial efforts to com- municate the distinction are required before, dur- ing and after implementation, and proactive steps to avoid blame, finger-pointing and punitive behaviours are essential to support the rhetoric if culture change is to be successfully brought about. Training and coaching at all levels may even be required. On the other hand, transparency of improvement can be very motivating to those involved. Indeed, the most positive feedback we received about the funnels came from staff who were directly involved in running Assay 3. They could see the problem, the result of their interven- tions, and the magnitude of their improvements directly. They were proud of their achievements, and were proud to know that everyone else could see those improvements too.
Figure 4: Testing workflow time-stamps. Choosing the most appropriate time stamps is an important precursor to maximising the use of the funnel diagrams
Drug Discovery World Winter 2011/12
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